ҮЛЕСТІРІЛГЕН ТАЛШЫҚТЫ-ОПТИКАЛЫҚ ДАТЧИКТЕР НЕГІЗІНДЕ КЕҢЕЙТІЛГЕН ОБЪЕКТІЛЕРДІҢ ТҰТАСТЫҒЫН БАҚЫЛАУДЫҢ ИНТЕЛЛЕКТУАЛДЫ ЖҮЙЕЛЕРІНЕ ШОЛУ

Abstract

This article provides an overview of existing intelligent systems for monitoring the integrity of extended objects based on distributed fiber-optic sensors. The results of developing measuring systems using distributed fiber-optic sensors are presented. The analysis of existing solutions for constructing measuring systems is carried out. The main types of measuring schemes and principles of determining the integrity of extended objects are considered. The basic principles for the construction of fiber-optic sensors are defined. The analysis demonstrated achievements in improving measurement accuracy using various optical reflectometry methods. Shortcomings of measuring systems are revealed, and ways of elimination are established. Methods of filtration from overlays caused by mechanical overvoltages and temperature influences are considered. The difficulties of applying well-known works are determined. A review of the current state of development of artificial intelligence in the field of its application in measurement systems is carried out. It is revealed that the systems built based on an optical time domain reflectometer (OTDR) using a convolutional neural network (CNN) show higher quality indicators. The possibilities ofusing different types of neural networks to recognize various mechanical influences using machine learning are considered. The disadvantages and advantages of using neural network systems in measuring systems based on distributed fiber-optic sensors are identified, and the most optimal type is selected. The direction for further research and development of a technical condition monitoring system based on distributed fiber-optic sensors has been determined.